Introducing Daggr: Chain apps programmatically, inspect visually

Back to Articles Introducing Daggr: Chain apps programmatically, inspect visually Published January 29, 2026 Update on GitHub Upvote 98 +92 merve merve Follow yuvraj sharma ysharma Follow Abubakar Abid abidlabs Follow hysts hysts Follow Pedro Cuenca pcuenq Follow TL;DR: Daggr is a new, open-source Python library for building AI workflows that connect Gradio apps, ML models, and custom functions. It automatically generates a visual canvas where you can inspect intermediate outputs, rerun individual steps, and manage state for complex pipelines, all in a few lines of Python code! Table of Contents Background Getting Started Sharing Your Workflows End-to-End Example with Different Nodes Next Steps Background If you've built AI applications that combine multiple models or processing steps, you know the pain: chaining API calls, debugging pipelines, and losing track of intermediate results. When something goes wrong in step 5 of a 10-step workflow, you often have to re-run everything just to see what happened. Most developers either build fragile scripts that are hard to debug or turn to heavy orchestration platforms designed for production pipelines—not rapid experimentation. We've been working on Daggr to solve problems we kept running into when building AI demos and workflows: Visualize your code flow: Unlike node-based GUI editors, where you drag and connect nodes visually, Daggr takes a code-first approach. You define workflows in Python, and a visual canvas is generated a...